An improved stability criterion for discrete-time time-delayed Lur’e systemwith sector-bounded nonlinearities

The absolute stability problem of discrete-time time-delayed Lur\u27e systems with sector bounded nonlinearities is investigated in this paper. Firstly, a modified Lyapunov-Krasovskii functional (LKF) is designed with augmenting additional double summation terms, which complements more coupling information between the delay intervals and other system state variables than some previous LKFs. Secondly, some improved delay-dependent absolute stability criteria based on linear matrix inequality form (LMI) are proposed via the modified LKF and the relaxed free-matrix-based summation inequality technique application. The stability criteria are less conservative than some results previously proposed. The reduction of the conservatism mainly relies on the full use of the relaxed summation inequality technique based on the modified LKF. Finally, two common numerical examples are presented to show the effectiveness of the proposed approach

Supersolid and pair correlations of the extended Jaynes-Cummings-Hubbard model on triangular lattices

We study the extended Jaynes-Cummings-Hubbard model on triangular cavity lattices and zigzag ladders. By using density-matrix renormalization group methods, we observe various types of solids with different density patterns and find evidence for light supersolids, which exist in extended regions of the phase diagram of the zigzag ladder. Furthermore, we observe strong pair correlations in the supersolid phase due to the interplay between the atoms in the cavities and atom-photon interaction. By means of cluster mean-field simulations and a scaling of the cluster size extending our analysis to two-dimensional triangular lattices, we present evidence for the emergence of a light supersolid in this case also.Comment: 11 pages, 16 figure

A Simulation Platform for Quantum Key Distribution Protocol

Abstract. Quantum key distribution protocol is a hot spot of research in the information security, so, lots of Quantum key distribution protocol appeared. Every quantum protocol needs to be confirmed whether it is feasible and safe, if all those quantum protocols use physics experiment to verify, it is very complex and expensive. So, we introduce the platform, a functional platform for quantum key distribution protocols simulation. This platform introduces communication receiver, sender, establishment and protocol selection etc and it was designed on .NET IDE, the main programming language is C#. We simulated the existing BB84 protocol on this platform, and got the simulation results which were completely tallied with theoretic results

WaterBox: A Testbed for Monitoring and Controlling Smart Water Networks

Copyright 2015 ACM.Smart water distribution networks are a good example of a large scale Cyber-Physical System that requires monitoring for precise data analysis and network control. Due to the critical nature of water distribution, an extensive simulation of decision making and control algorithms are required before their deployment. Although some aspects of water network behaviour can be simulated in software such as hydraulic responses in valve changes, software simulators are unable to include dynamic events such as leakages or bursts in physical models. Furthermore, due to safety concerns, contemporary large-scale testbeds are limited to the monitoring processes or control methods with well established safety guarantees. Sophisticated algorithms for dynamic and optimal water network reconfiguration are not yet widespread. This paper presents a small-scale testbed, WaterBox, which allows the simulation of emerging/advanced monitoring and control algorithms in a fail-safe environment. The flexible hydraulic, hardware, and software infrastructure enables a substantial number of experiments. On-going experiments are related to in-node data processing and decision making, energy optimization, event-driven communication, and automatic control

PRE-PURCHASE AND POST-PURCHASE SALES PROMOTIONS ON E-COMMERCE PLATFORMS: THE EFFECTS OF PROMOTIONAL BENEFITS ON CUSTOMER-BASED BRAND EQUITY

This research examines the impacts of electronic commerce platforms\u27 sales promotions\u27 benefits on customer-based brand equity (platform brand awareness and platform brand association) and how these relationships are moderated by the promotion stage. Based on the two functions of sales promotions (stimulation vs. maintenance), we propose a five-benefit framework consisting of exploration, convenience, savings, social bonds and structural bonds. Our results reveal the two functions of sales promotions and the positive effects of the benefits on customer-based brand equity (CBBE). The differences between pre- and post-purchase sales promotions are also significant. We discuss the managerial and theoretical implications of these results at the end

A Variable Neighborhood MOEA/D for Multiobjective Test Task Scheduling Problem

Test task scheduling problem (TTSP) is a typical combinational optimization scheduling problem. This paper proposes a variable neighborhood MOEA/D (VNM) to solve the multiobjective TTSP. Two minimization objectives, the maximal completion time (makespan) and the mean workload, are considered together. In order to make solutions obtained more close to the real Pareto Front, variable neighborhood strategy is adopted. Variable neighborhood approach is proposed to render the crossover span reasonable. Additionally, because the search space of the TTSP is so large that many duplicate solutions and local optima will exist, the Starting Mutation is applied to prevent solutions from becoming trapped in local optima. It is proved that the solutions got by VNM can converge to the global optimum by using Markov Chain and Transition Matrix, respectively. The experiments of comparisons of VNM, MOEA/D, and CNSGA (chaotic nondominated sorting genetic algorithm) indicate that VNM performs better than the MOEA/D and the CNSGA in solving the TTSP. The results demonstrate that proposed algorithm VNM is an efficient approach to solve the multiobjective TTSP

Edge-aware Hard Clustering Graph Pooling for Brain Imaging Data

Graph Convolutional Networks (GCNs) can capture non-Euclidean spatial dependence between different brain regions, and the graph pooling operator in GCNs is key to enhancing the representation learning capability and acquiring abnormal brain maps. However, the majority of existing research designs graph pooling operators only from the perspective of nodes while disregarding the original edge features, in a way that not only confines graph pooling application scenarios, but also diminishes its ability to capture critical substructures. In this study, a clustering graph pooling method that first supports multidimensional edge features, called Edge-aware hard clustering graph pooling (EHCPool), is developed. EHCPool proposes the first 'Edge-to-node' score evaluation criterion based on edge features to assess node feature significance. To more effectively capture the critical subgraphs, a novel Iteration n-top strategy is further designed to adaptively learn sparse hard clustering assignments for graphs. Subsequently, an innovative N-E Aggregation strategy is presented to aggregate node and edge feature information in each independent subgraph. The proposed model was evaluated on multi-site brain imaging public datasets and yielded state-of-the-art performance. We believe this method is the first deep learning tool with the potential to probe different types of abnormal functional brain networks from data-driven perspective. Core code is at: https://github.com/swfen/EHCPool

Cappy: Outperforming and Boosting Large Multi-Task LMs with a Small Scorer

Large language models (LLMs) such as T0, FLAN, and OPT-IML, excel in multi-tasking under a unified instruction-following paradigm, where they also exhibit remarkable generalization abilities to unseen tasks. Despite their impressive performance, these LLMs, with sizes ranging from several billion to hundreds of billions of parameters, demand substantial computational resources, making their training and inference expensive and inefficient. Furthermore, adapting these models to downstream applications, particularly complex tasks, is often unfeasible due to the extensive hardware requirements for finetuning, even when utilizing parameter-efficient approaches such as prompt tuning. Additionally, the most powerful multi-task LLMs, such as OPT-IML-175B and FLAN-PaLM-540B, are not publicly accessible, severely limiting their customization potential. To address these challenges, we introduce a pretrained small scorer, Cappy, designed to enhance the performance and efficiency of multi-task LLMs. With merely 360 million parameters, Cappy functions either independently on classification tasks or serve as an auxiliary component for LLMs, boosting their performance. Moreover, Cappy enables efficiently integrating downstream supervision without requiring LLM finetuning nor the access to their parameters. Our experiments demonstrate that, when working independently on 11 language understanding tasks from PromptSource, Cappy outperforms LLMs that are several orders of magnitude larger. Besides, on 45 complex tasks from BIG-Bench, Cappy boosts the performance of the advanced multi-task LLM, FLAN-T5, by a large margin. Furthermore, Cappy is flexible to cooperate with other LLM adaptations, including finetuning and in-context learning, offering additional performance enhancement.Comment: In proceedings of NeurIPS 2023; Code and model available at https://github.com/tanyuqian/cappy and https://huggingface.co/btan2/cappy-large, respectivel

STROBE-GnRHa pretreatment in frozen-embryo transfer cycles improves clinical outcomes for patients with persistent thin endometrium: A case-control study.

The well-prepared endometrium with appropriate thickness plays a critical role in successful embryo implantation. The thin endometrium is the main factor of frozen-embryo transfer (FET), resulting in the failure of implantation undergoing FET. Hormone treatment is suggested to improve endometrium thickness; however, among the larger numbers of cases, it cannot reach the sufficient thickness, which leads to a high cancelation rate of embryo transfer as well as waste high-quality embryos. Thus, it increases the burden to patients in both economic and psychological perspectives. We performed a retrospective observational study, which was composed with 2 cohorts, either with the conventional hormone replacement therapy (HRT) protocol or HRT with gonadotrophin-releasing hormone agonist (GnRHa) pretreatment to prepare the endometrium before FET. The measurements of endometrium thickness, hormone level, transfer cycle cancelation rate, pregnancy rate, and implantation rate were retrieved from the medical records during the routine clinic visits until 1 month after embryo transfer. The comparisons between 2 cohorts were performed by t-test or Mann-Whitney U test depending on the different attributions of data. In total, 49 cycles were under HRT with GnRHa pretreatment and 84 cycles were under the conventional HRT protocol. HRT with GnRHa pretreatment group improved the endometrial thickness (8.13 ± 1.79 vs 7.51 ± 1.45, P = .031), decreased the transfer cancelation rate (P = .003), and increased clinical pregnancy rate and implantation rate significantly (both P = .001). Additionally, luteinizing hormone level in pretreatment group was consistently lower than conventional HRT group (P < .05). Our study revealed HRT with GnRHa pretreatment efficiently improved the endometrial thickness, therefore, decreased the FET cycle cancelation. It also elevated the embryo implantation rate and clinical pregnancy rate by improving endometrial receptivity